The easiest way to do machine learning
Project description
#ML
This module provides for the easiest way to implement Machine Learning algoritms without the need to know about them.
Use this module if
- You are a complete beginner to Machine Learning.
- You find other modules too complicated.
This module is not meant for high level tasks, but only for simple use and learning.
I would not recommend using this module for big projects.
This module uses a tensorflow backend.
Install by running
pip install ml-python
Or by cloning the repo and installing it.
git clone https://github.com/vivek3141/ml
cd ml
python setup.py install
This module has support for ANNs, CNNs, linear regression, logistic regression, k-means.
##Examples
Examples for all implemented structures can be found in /examples
.
In this example, we will see how to learn a linear regression example.
First, import the required modules.
import numpy as np
from ml.linear_regression import LinearRegression
Then make the required object
l = LinearRegression()
This code below randomly generates 50 data points from 0 to 10 for us to run linear regression on.
# Randomly generating the data
x = np.array(list(map(int, 10*np.random.random(50))))
y = np.array(list(map(int, 10*np.random.random(50))))
Lastly, train it. Set graph=True
to visualize the dataset and the model.
l.fit(data=x, labels=y, graph=True)
The full code can be found in /examples/linear_regression.py
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